arXiv:2505. 15998v4 Announce Type: replace Abstract: We present a curiosity-driven AI scientist method for discovering system-level dynamics in Flow-Lenia, a continuous cellular automaton (CA) with mass conservation and parameter localization.
arXiv:2608. 06871v1 Announce Type: new Abstract: Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent behavior, with applications from population dynamics and biology to economic policy and strategic decision-making.
arXiv:2606. 13662v1 Announce Type: new Abstract: LLM-based agents have shown increasing potential in automating scientific discovery.
The paper introduces a closed‑loop framework using autotelic reinforcement learning to explore and manipulate complex systems, specifically Lenia, a continuous cellular automaton. An agent called CARL autonomously samples diverse goals and learns a goal‑conditioned policy that intervenes with minimal, local perturbations. CARL demonstrates three key abilities: discovering stable solitons more efficiently than heuristic baselines, steering existing solitons with few interventions, and enabling humans to guide solitons through maze environments in real time via high‑level commands. The agents generalize zero‑shot to out‑of‑distribution conditions, suggesting a path toward artificial experimentalist agents that can discover and control emergent phenomena.
The paper investigates a cellular‑automata substrate where each pixel hosts a tiny neural network that senses its neighbors and survives only by self‑replication with mutated weights. Starting from a few founders, the system evolves into a spatially organized ecosystem of competing species, and the authors introduce coarse‑grained metrics to quantify phenotypic and genotypic diversity at two scales. Experiments show that high phenotypic diversity reduces genotypic diversity and vice versa, and that full‑genome hash colouring reveals lineage structures missed by random‑weight probes.
The paper introduces PACEvolve, a framework that improves self‑evolving agents powered by Large Language Models by addressing their tendency to become trapped in local contexts and repeat flawed hypotheses. It does so through three techniques: Hierarchical Context Management to prune memory, Momentum‑Based Backtracking to escape local minima, and a self‑adaptive Collaborative Evolution policy to balance refinement and knowledge transfer. These methods enable the agents to maintain a global view of search momentum and achieve state‑of‑the‑art results on complex evolutionary benchmarks.
CORAL is a framework that enables autonomous multi‑agent evolution for open‑ended discovery, replacing rigid heuristics with long‑running agents that explore, reflect, and collaborate via shared memory and asynchronous execution. It incorporates safeguards such as isolated workspaces, evaluator separation, and resource management. In experiments across mathematical, algorithmic, and systems optimization tasks, CORAL achieves 3–10 times higher improvement rates with fewer evaluations than traditional evolutionary baselines, and improves the best known score on Anthropic’s kernel engineering task from 1363 to 1103 cycles.
arXiv:2604. 19341v2 Announce Type: replace-cross Abstract: Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions.
Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inference (TI) is critical.
arXiv:2607. 07467v1 Announce Type: new Abstract: Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics.
arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?
arXiv:2606. 25198v2 Announce Type: replace Abstract: Autonomous AI Research promises to accelerate the scientific progress of machine learning.
arXiv:2604. 22748v2 Announce Type: replace Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck.